Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations39737
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory128.0 B

Variable types

Text3
Numeric9
Categorical2
DateTime1

Alerts

latitude is highly overall correlated with neighborhood_groupHigh correlation
longitude is highly overall correlated with neighborhood_groupHigh correlation
neighborhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation

Reproduction

Analysis started2025-01-30 13:50:32.238024
Analysis finished2025-01-30 13:50:56.754838
Duration24.52 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

name
Text

Distinct39017
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size620.9 KiB
2025-01-30T13:50:57.354994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length179
Median length70
Mean length36.427461
Min length1

Characters and Unicode

Total characters1447518
Distinct characters735
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38545 ?
Unique (%)97.0%

Sample

1st rowSkylit Midtown Castle
2nd rowTHE VILLAGE OF HARLEM....NEW YORK !
3rd rowCozy Entire Floor of Brownstone
4th rowEntire Apt: Spacious Studio/Loft by central park
5th rowLarge Cozy 1 BR Apartment In Midtown East
ValueCountFrequency (%)
in 14237
 
6.0%
room 8893
 
3.7%
private 6344
 
2.7%
bedroom 6282
 
2.6%
6068
 
2.5%
apartment 5658
 
2.4%
cozy 4486
 
1.9%
apt 3718
 
1.6%
brooklyn 3547
 
1.5%
the 3266
 
1.4%
Other values (10349) 176699
73.9%
2025-01-30T13:50:58.366575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200767
 
13.9%
e 100522
 
6.9%
o 100458
 
6.9%
t 85893
 
5.9%
a 85139
 
5.9%
r 79916
 
5.5%
i 77788
 
5.4%
n 76899
 
5.3%
l 41994
 
2.9%
m 40929
 
2.8%
Other values (725) 557213
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1447518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
200767
 
13.9%
e 100522
 
6.9%
o 100458
 
6.9%
t 85893
 
5.9%
a 85139
 
5.9%
r 79916
 
5.5%
i 77788
 
5.4%
n 76899
 
5.3%
l 41994
 
2.9%
m 40929
 
2.8%
Other values (725) 557213
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1447518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
200767
 
13.9%
e 100522
 
6.9%
o 100458
 
6.9%
t 85893
 
5.9%
a 85139
 
5.9%
r 79916
 
5.5%
i 77788
 
5.4%
n 76899
 
5.3%
l 41994
 
2.9%
m 40929
 
2.8%
Other values (725) 557213
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1447518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
200767
 
13.9%
e 100522
 
6.9%
o 100458
 
6.9%
t 85893
 
5.9%
a 85139
 
5.9%
r 79916
 
5.5%
i 77788
 
5.4%
n 76899
 
5.3%
l 41994
 
2.9%
m 40929
 
2.8%
Other values (725) 557213
38.5%

host_id
Real number (ℝ)

Distinct32367
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66218061
Minimum2571
Maximum2.7432131 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:50:58.598838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2571
5-th percentile809681.2
Q17824750
median30736639
Q31.0361186 × 108
95-th percentile2.396412 × 108
Maximum2.7432131 × 108
Range2.7431874 × 108
Interquartile range (IQR)95787113

Descriptive statistics

Standard deviation77502126
Coefficient of variation (CV)1.1704077
Kurtosis0.30851014
Mean66218061
Median Absolute Deviation (MAD)27174331
Skewness1.2494698
Sum2.6313071 × 1012
Variance6.0065795 × 1015
MonotonicityNot monotonic
2025-01-30T13:50:58.980700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 144
 
0.4%
190921808 38
 
0.1%
119669058 34
 
0.1%
213781715 31
 
0.1%
224414117 29
 
0.1%
417504 23
 
0.1%
252604696 20
 
0.1%
134184451 18
 
< 0.1%
201015598 17
 
< 0.1%
159091490 17
 
< 0.1%
Other values (32357) 39366
99.1%
ValueCountFrequency (%)
2571 1
 
< 0.1%
2787 5
< 0.1%
2845 2
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
3647 2
 
< 0.1%
3867 2
 
< 0.1%
ValueCountFrequency (%)
274321313 1
< 0.1%
274311461 1
< 0.1%
274307600 1
< 0.1%
274298453 1
< 0.1%
274273284 1
< 0.1%
274225617 1
< 0.1%
274195458 1
< 0.1%
274188386 1
< 0.1%
274103383 1
< 0.1%
274040642 1
< 0.1%
Distinct10347
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size620.9 KiB
2025-01-30T13:50:59.573384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.0734832
Min length1

Characters and Unicode

Total characters241342
Distinct characters186
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6299 ?
Unique (%)15.9%

Sample

1st rowJennifer
2nd rowElisabeth
3rd rowLisaRoxanne
4th rowLaura
5th rowChris
ValueCountFrequency (%)
877
 
2.0%
and 510
 
1.2%
michael 365
 
0.8%
david 359
 
0.8%
john 268
 
0.6%
alex 260
 
0.6%
sarah 220
 
0.5%
maria 209
 
0.5%
daniel 202
 
0.5%
jessica 182
 
0.4%
Other values (9354) 40569
92.2%
2025-01-30T13:51:00.326302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 31078
 
12.9%
e 23296
 
9.7%
i 20143
 
8.3%
n 19494
 
8.1%
r 14173
 
5.9%
l 12539
 
5.2%
o 10024
 
4.2%
t 7653
 
3.2%
s 7608
 
3.2%
h 7577
 
3.1%
Other values (176) 87757
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 31078
 
12.9%
e 23296
 
9.7%
i 20143
 
8.3%
n 19494
 
8.1%
r 14173
 
5.9%
l 12539
 
5.2%
o 10024
 
4.2%
t 7653
 
3.2%
s 7608
 
3.2%
h 7577
 
3.1%
Other values (176) 87757
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 31078
 
12.9%
e 23296
 
9.7%
i 20143
 
8.3%
n 19494
 
8.1%
r 14173
 
5.9%
l 12539
 
5.2%
o 10024
 
4.2%
t 7653
 
3.2%
s 7608
 
3.2%
h 7577
 
3.1%
Other values (176) 87757
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 31078
 
12.9%
e 23296
 
9.7%
i 20143
 
8.3%
n 19494
 
8.1%
r 14173
 
5.9%
l 12539
 
5.2%
o 10024
 
4.2%
t 7653
 
3.2%
s 7608
 
3.2%
h 7577
 
3.1%
Other values (176) 87757
36.4%

neighborhood_group
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size620.9 KiB
Brooklyn
17347 
Manhattan
16009 
Queens
5027 
Bronx
 
1010
Staten Island
 
344

Length

Max length13
Median length9
Mean length8.1168936
Min length5

Characters and Unicode

Total characters322541
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManhattan
2nd rowManhattan
3rd rowBrooklyn
4th rowManhattan
5th rowManhattan

Common Values

ValueCountFrequency (%)
Brooklyn 17347
43.7%
Manhattan 16009
40.3%
Queens 5027
 
12.7%
Bronx 1010
 
2.5%
Staten Island 344
 
0.9%

Length

2025-01-30T13:51:00.611262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T13:51:01.136720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 17347
43.3%
manhattan 16009
39.9%
queens 5027
 
12.5%
bronx 1010
 
2.5%
staten 344
 
0.9%
island 344
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 56090
17.4%
a 48715
15.1%
o 35704
11.1%
t 32706
10.1%
r 18357
 
5.7%
B 18357
 
5.7%
l 17691
 
5.5%
y 17347
 
5.4%
k 17347
 
5.4%
M 16009
 
5.0%
Other values (10) 44218
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 322541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 56090
17.4%
a 48715
15.1%
o 35704
11.1%
t 32706
10.1%
r 18357
 
5.7%
B 18357
 
5.7%
l 17691
 
5.5%
y 17347
 
5.4%
k 17347
 
5.4%
M 16009
 
5.0%
Other values (10) 44218
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 322541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 56090
17.4%
a 48715
15.1%
o 35704
11.1%
t 32706
10.1%
r 18357
 
5.7%
B 18357
 
5.7%
l 17691
 
5.5%
y 17347
 
5.4%
k 17347
 
5.4%
M 16009
 
5.0%
Other values (10) 44218
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 322541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 56090
17.4%
a 48715
15.1%
o 35704
11.1%
t 32706
10.1%
r 18357
 
5.7%
B 18357
 
5.7%
l 17691
 
5.5%
y 17347
 
5.4%
k 17347
 
5.4%
M 16009
 
5.0%
Other values (10) 44218
13.7%
Distinct219
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:01.468828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.921635
Min length4

Characters and Unicode

Total characters473730
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowMidtown
2nd rowHarlem
3rd rowClinton Hill
4th rowEast Harlem
5th rowMurray Hill
ValueCountFrequency (%)
east 5353
 
8.4%
side 3410
 
5.4%
williamsburg 3363
 
5.3%
harlem 3277
 
5.2%
bedford-stuyvesant 3242
 
5.1%
heights 3155
 
5.0%
upper 2664
 
4.2%
village 2509
 
3.9%
bushwick 2153
 
3.4%
west 1957
 
3.1%
Other values (231) 32443
51.1%
2025-01-30T13:51:01.935394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 43042
 
9.1%
i 33346
 
7.0%
s 32865
 
6.9%
a 31054
 
6.6%
t 31027
 
6.5%
l 27898
 
5.9%
r 27565
 
5.8%
23789
 
5.0%
n 21420
 
4.5%
o 20070
 
4.2%
Other values (44) 181654
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 473730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 43042
 
9.1%
i 33346
 
7.0%
s 32865
 
6.9%
a 31054
 
6.6%
t 31027
 
6.5%
l 27898
 
5.9%
r 27565
 
5.8%
23789
 
5.0%
n 21420
 
4.5%
o 20070
 
4.2%
Other values (44) 181654
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 473730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 43042
 
9.1%
i 33346
 
7.0%
s 32865
 
6.9%
a 31054
 
6.6%
t 31027
 
6.5%
l 27898
 
5.9%
r 27565
 
5.8%
23789
 
5.0%
n 21420
 
4.5%
o 20070
 
4.2%
Other values (44) 181654
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 473730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 43042
 
9.1%
i 33346
 
7.0%
s 32865
 
6.9%
a 31054
 
6.6%
t 31027
 
6.5%
l 27898
 
5.9%
r 27565
 
5.8%
23789
 
5.0%
n 21420
 
4.5%
o 20070
 
4.2%
Other values (44) 181654
38.3%

latitude
Real number (ℝ)

High correlation 

Distinct17805
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.727575
Minimum40.49979
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:02.085154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.643716
Q140.68808
median40.72008
Q340.76326
95-th percentile40.828082
Maximum40.91306
Range0.41327
Interquartile range (IQR)0.07518

Descriptive statistics

Standard deviation0.056292643
Coefficient of variation (CV)0.0013821752
Kurtosis0.060462811
Mean40.727575
Median Absolute Deviation (MAD)0.03622
Skewness0.29695214
Sum1618391.7
Variance0.0031688616
MonotonicityNot monotonic
2025-01-30T13:51:02.285192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 17
 
< 0.1%
40.70766 11
 
< 0.1%
40.68444 11
 
< 0.1%
40.71353 11
 
< 0.1%
40.68634 11
 
< 0.1%
40.69414 11
 
< 0.1%
40.68374 10
 
< 0.1%
40.69454 10
 
< 0.1%
40.68683 10
 
< 0.1%
40.72085 10
 
< 0.1%
Other values (17795) 39625
99.7%
ValueCountFrequency (%)
40.49979 1
< 0.1%
40.50641 1
< 0.1%
40.50708 1
< 0.1%
40.50868 1
< 0.1%
40.50873 1
< 0.1%
40.50943 1
< 0.1%
40.51133 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.527 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.91234 1
< 0.1%
40.91167 1
< 0.1%
40.90804 1
< 0.1%
40.90734 1
< 0.1%
40.90484 1
< 0.1%
40.90406 1
< 0.1%
40.90391 1
< 0.1%
40.90356 1
< 0.1%
40.90329 1
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct13979
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.949145
Minimum-74.24442
Maximum-73.71299
Zeros0
Zeros (%)0.0%
Negative39737
Negative (%)100.0%
Memory size620.9 KiB
2025-01-30T13:51:02.509807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.24442
5-th percentile-74.00262
Q1-73.98104
median-73.95332
Q3-73.93217
95-th percentile-73.85607
Maximum-73.71299
Range0.53143
Interquartile range (IQR)0.04887

Descriptive statistics

Standard deviation0.047708551
Coefficient of variation (CV)-0.00064515352
Kurtosis4.6640199
Mean-73.949145
Median Absolute Deviation (MAD)0.02496
Skewness1.2118019
Sum-2938517.2
Variance0.0022761059
MonotonicityNot monotonic
2025-01-30T13:51:02.732967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.95332 16
 
< 0.1%
-73.95677 16
 
< 0.1%
-73.94791 16
 
< 0.1%
-73.9506 15
 
< 0.1%
-73.9435 14
 
< 0.1%
-73.94537 14
 
< 0.1%
-73.95551 14
 
< 0.1%
-73.98439 14
 
< 0.1%
-73.95427 14
 
< 0.1%
-73.95136 14
 
< 0.1%
Other values (13969) 39590
99.6%
ValueCountFrequency (%)
-74.24442 1
< 0.1%
-74.24285 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.20941 1
< 0.1%
ValueCountFrequency (%)
-73.71299 1
< 0.1%
-73.7169 1
< 0.1%
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.71928 1
< 0.1%
-73.72173 1
< 0.1%
-73.72179 1
< 0.1%
-73.72247 1
< 0.1%
-73.72435 1
< 0.1%
-73.72581 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size620.9 KiB
Private room
19865 
Entire home/apt
18882 
Shared room
 
990

Length

Max length15
Median length12
Mean length13.400609
Min length11

Characters and Unicode

Total characters532500
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowPrivate room
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Private room 19865
50.0%
Entire home/apt 18882
47.5%
Shared room 990
 
2.5%

Length

2025-01-30T13:51:02.927775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T13:51:03.041585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
room 20855
26.2%
private 19865
25.0%
entire 18882
23.8%
home/apt 18882
23.8%
shared 990
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o 60592
11.4%
r 60592
11.4%
e 58619
11.0%
t 57629
10.8%
m 39737
7.5%
a 39737
7.5%
39737
7.5%
i 38747
 
7.3%
h 19872
 
3.7%
P 19865
 
3.7%
Other values (7) 97373
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 60592
11.4%
r 60592
11.4%
e 58619
11.0%
t 57629
10.8%
m 39737
7.5%
a 39737
7.5%
39737
7.5%
i 38747
 
7.3%
h 19872
 
3.7%
P 19865
 
3.7%
Other values (7) 97373
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 60592
11.4%
r 60592
11.4%
e 58619
11.0%
t 57629
10.8%
m 39737
7.5%
a 39737
7.5%
39737
7.5%
i 38747
 
7.3%
h 19872
 
3.7%
P 19865
 
3.7%
Other values (7) 97373
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 60592
11.4%
r 60592
11.4%
e 58619
11.0%
t 57629
10.8%
m 39737
7.5%
a 39737
7.5%
39737
7.5%
i 38747
 
7.3%
h 19872
 
3.7%
P 19865
 
3.7%
Other values (7) 97373
18.3%

price
Real number (ℝ)

Distinct318
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.02768
Minimum10
Maximum334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:03.187770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q165
median100
Q3155
95-th percentile250
Maximum334
Range324
Interquartile range (IQR)90

Descriptive statistics

Standard deviation67.161063
Coefficient of variation (CV)0.56424743
Kurtosis0.26883518
Mean119.02768
Median Absolute Deviation (MAD)40
Skewness0.9609735
Sum4729803
Variance4510.6084
MonotonicityNot monotonic
2025-01-30T13:51:03.396581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1855
 
4.7%
150 1782
 
4.5%
50 1353
 
3.4%
60 1310
 
3.3%
75 1276
 
3.2%
200 1239
 
3.1%
80 1164
 
2.9%
65 1084
 
2.7%
70 1064
 
2.7%
120 999
 
2.5%
Other values (308) 26611
67.0%
ValueCountFrequency (%)
10 16
< 0.1%
11 3
 
< 0.1%
12 3
 
< 0.1%
13 1
 
< 0.1%
15 5
 
< 0.1%
16 5
 
< 0.1%
18 2
 
< 0.1%
19 3
 
< 0.1%
20 28
0.1%
21 6
 
< 0.1%
ValueCountFrequency (%)
334 1
 
< 0.1%
333 6
 
< 0.1%
332 1
 
< 0.1%
331 1
 
< 0.1%
330 24
 
0.1%
329 10
 
< 0.1%
328 3
 
< 0.1%
327 1
 
< 0.1%
325 111
0.3%
324 2
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6962025
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:03.583073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8635408
Coefficient of variation (CV)0.69117241
Kurtosis2.4854943
Mean2.6962025
Median Absolute Deviation (MAD)1
Skewness1.5375781
Sum107139
Variance3.4727843
MonotonicityNot monotonic
2025-01-30T13:51:03.707786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 12066
30.4%
2 11080
27.9%
3 7375
18.6%
4 3066
 
7.7%
5 2821
 
7.1%
7 1951
 
4.9%
6 679
 
1.7%
10 462
 
1.2%
8 127
 
0.3%
9 79
 
0.2%
ValueCountFrequency (%)
1 12066
30.4%
2 11080
27.9%
3 7375
18.6%
4 3066
 
7.7%
5 2821
 
7.1%
6 679
 
1.7%
7 1951
 
4.9%
8 127
 
0.3%
9 79
 
0.2%
10 462
 
1.2%
ValueCountFrequency (%)
11 31
 
0.1%
10 462
 
1.2%
9 79
 
0.2%
8 127
 
0.3%
7 1951
 
4.9%
6 679
 
1.7%
5 2821
 
7.1%
4 3066
 
7.7%
3 7375
18.6%
2 11080
27.9%

number_of_reviews
Real number (ℝ)

High correlation 

Distinct391
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.728478
Minimum1
Maximum629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:03.877059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median17
Q331.728478
95-th percentile124
Maximum629
Range628
Interquartile range (IQR)27.728478

Descriptive statistics

Standard deviation45.964693
Coefficient of variation (CV)1.4486889
Kurtosis17.831984
Mean31.728478
Median Absolute Deviation (MAD)14.728478
Skewness3.4592964
Sum1260794.5
Variance2112.753
MonotonicityNot monotonic
2025-01-30T13:51:04.078364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.72847802 6701
 
16.9%
1 4047
 
10.2%
2 2794
 
7.0%
3 2033
 
5.1%
4 1631
 
4.1%
5 1288
 
3.2%
6 1137
 
2.9%
7 969
 
2.4%
8 959
 
2.4%
9 809
 
2.0%
Other values (381) 17369
43.7%
ValueCountFrequency (%)
1 4047
10.2%
2 2794
7.0%
3 2033
5.1%
4 1631
4.1%
5 1288
 
3.2%
6 1137
 
2.9%
7 969
 
2.4%
8 959
 
2.4%
9 809
 
2.0%
10 666
 
1.7%
ValueCountFrequency (%)
629 1
< 0.1%
607 1
< 0.1%
597 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
543 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
480 1
< 0.1%
Distinct1696
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size620.9 KiB
Minimum2011-03-28 00:00:00
Maximum2019-07-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-30T13:51:04.282239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:51:04.498122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

High correlation 

Distinct937
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4637252
Minimum0.01
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:04.724219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.29
median1.25
Q31.89
95-th percentile4.562
Maximum58.5
Range58.49
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.5899264
Coefficient of variation (CV)1.0862192
Kurtosis51.348297
Mean1.4637252
Median Absolute Deviation (MAD)0.88
Skewness3.3957793
Sum58164.047
Variance2.5278658
MonotonicityNot monotonic
2025-01-30T13:51:04.923151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.373251377 6701
 
16.9%
0.02 811
 
2.0%
0.05 737
 
1.9%
1 721
 
1.8%
0.03 666
 
1.7%
0.16 537
 
1.4%
0.04 518
 
1.3%
0.08 486
 
1.2%
0.09 452
 
1.1%
0.06 450
 
1.1%
Other values (927) 27658
69.6%
ValueCountFrequency (%)
0.01 30
 
0.1%
0.02 811
2.0%
0.03 666
1.7%
0.04 518
1.3%
0.05 737
1.9%
0.06 450
1.1%
0.07 359
0.9%
0.08 486
1.2%
0.09 452
1.1%
0.1 357
0.9%
ValueCountFrequency (%)
58.5 1
< 0.1%
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.81 1
< 0.1%
16.22 1
< 0.1%
16.03 1
< 0.1%
15.78 1
< 0.1%
15.32 1
< 0.1%
Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0729798
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:05.089398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation19.744108
Coefficient of variation (CV)6.425069
Kurtosis259.63234
Mean3.0729798
Median Absolute Deviation (MAD)0
Skewness16.026103
Sum122111
Variance389.82979
MonotonicityNot monotonic
2025-01-30T13:51:05.246659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 27692
69.7%
2 5834
 
14.7%
3 2454
 
6.2%
4 1142
 
2.9%
5 677
 
1.7%
6 403
 
1.0%
7 310
 
0.8%
8 263
 
0.7%
9 151
 
0.4%
327 144
 
0.4%
Other values (16) 667
 
1.7%
ValueCountFrequency (%)
1 27692
69.7%
2 5834
 
14.7%
3 2454
 
6.2%
4 1142
 
2.9%
5 677
 
1.7%
6 403
 
1.0%
7 310
 
0.8%
8 263
 
0.7%
9 151
 
0.4%
10 139
 
0.3%
ValueCountFrequency (%)
327 144
0.4%
47 38
 
0.1%
43 3
 
< 0.1%
34 34
 
0.1%
33 31
 
0.1%
30 29
 
0.1%
28 23
 
0.1%
26 13
 
< 0.1%
20 20
 
0.1%
18 18
 
< 0.1%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.52594
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.9 KiB
2025-01-30T13:51:05.431387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q190
median160.52594
Q3179
95-th percentile354
Maximum365
Range364
Interquartile range (IQR)89

Descriptive statistics

Standard deviation96.482529
Coefficient of variation (CV)0.6010401
Kurtosis-0.29181954
Mean160.52594
Median Absolute Deviation (MAD)44.525944
Skewness0.3979936
Sum6378819.4
Variance9308.8784
MonotonicityNot monotonic
2025-01-30T13:51:05.657982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160.5259438 15685
39.5%
365 755
 
1.9%
1 353
 
0.9%
5 307
 
0.8%
89 304
 
0.8%
3 275
 
0.7%
364 273
 
0.7%
179 239
 
0.6%
2 234
 
0.6%
90 231
 
0.6%
Other values (356) 21081
53.1%
ValueCountFrequency (%)
1 353
0.9%
2 234
0.6%
3 275
0.7%
4 210
0.5%
5 307
0.8%
6 227
0.6%
7 192
0.5%
8 215
0.5%
9 177
0.4%
10 151
0.4%
ValueCountFrequency (%)
365 755
1.9%
364 273
 
0.7%
363 180
 
0.5%
362 123
 
0.3%
361 82
 
0.2%
360 84
 
0.2%
359 119
 
0.3%
358 120
 
0.3%
357 66
 
0.2%
356 63
 
0.2%

Interactions

2025-01-30T13:50:53.948614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:34.823183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:36.470518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:39.038847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:44.682102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:46.616227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:48.180399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:49.776345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:52.321610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:54.144213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:35.057089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:36.637366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:39.351120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:45.037892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:46.818205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:48.357920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:49.967908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:52.495240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:54.312157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:35.232889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:36.806647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:39.810970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:45.287341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:46.984426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:48.526166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:51.076028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:52.662555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:54.500268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:35.390735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:36.982246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:40.162730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:45.515984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:47.144519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:48.708903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:51.238229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:52.883947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:54.672888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:35.556566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:37.299966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:40.592809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:45.727214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:47.308380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:48.927720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:51.424337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:53.078723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:54.848645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:35.714184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:37.639609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:41.234254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:45.921232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:47.456245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:49.086515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:51.577055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:53.236515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:55.056557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:35.925938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:37.932247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:42.695308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:46.097682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:47.617920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:49.261711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:51.744417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:53.406267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:55.241292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:36.109232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:38.214080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:43.303062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:46.267475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:47.823079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:49.432586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:51.967961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:53.576138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:55.442141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:36.270610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:38.677068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:43.784047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:46.440398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:48.011905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:49.600897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:52.140517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-30T13:50:53.745790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-30T13:51:05.821540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
availability_365calculated_host_listings_counthost_idlatitudelongitudeminimum_nightsneighborhood_groupnumber_of_reviewspricereviews_per_monthroom_type
availability_3651.0000.1630.031-0.0290.054-0.0810.0950.038-0.007-0.0350.122
calculated_host_listings_count0.1631.0000.135-0.0490.134-0.1500.0520.120-0.1920.1980.063
host_id0.0310.1351.0000.0400.146-0.1840.109-0.072-0.1100.2450.098
latitude-0.029-0.0490.0401.0000.056-0.0230.542-0.0040.114-0.0070.097
longitude0.0540.1340.1460.0561.000-0.0850.6540.050-0.4010.1090.125
minimum_nights-0.081-0.150-0.184-0.023-0.0851.0000.053-0.0990.118-0.2110.122
neighborhood_group0.0950.0520.1090.5420.6540.0531.0000.0220.1720.0450.089
number_of_reviews0.0380.120-0.072-0.0040.050-0.0990.0221.0000.0080.7060.027
price-0.007-0.192-0.1100.114-0.4010.1180.1720.0081.000-0.0160.491
reviews_per_month-0.0350.1980.245-0.0070.109-0.2110.0450.706-0.0161.0000.021
room_type0.1220.0630.0980.0970.1250.1220.0890.0270.4910.0211.000

Missing values

2025-01-30T13:50:55.886218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-30T13:50:56.344629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namehost_idhost_nameneighborhood_groupneighborhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
id
2595Skylit Midtown Castle2845JenniferManhattanMidtown40.75362-73.98377Entire home/apt225.0145.0000002019-05-210.3800002355.000000
3647THE VILLAGE OF HARLEM....NEW YORK !4632ElisabethManhattanHarlem40.80902-73.94190Private room150.0331.7284782019-07-081.3732511365.000000
3831Cozy Entire Floor of Brownstone4869LisaRoxanneBrooklynClinton Hill40.68514-73.95976Entire home/apt89.01270.0000002019-07-054.6400001194.000000
5022Entire Apt: Spacious Studio/Loft by central park7192LauraManhattanEast Harlem40.79851-73.94399Entire home/apt80.0109.0000002018-11-190.1000001160.525944
5099Large Cozy 1 BR Apartment In Midtown East7322ChrisManhattanMurray Hill40.74767-73.97500Entire home/apt200.0374.0000002019-06-220.5900001129.000000
5178Large Furnished Room Near B'way8967ShunichiManhattanHell's Kitchen40.76489-73.98493Private room79.02430.0000002019-06-243.4700001220.000000
5203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80178-73.96723Private room79.02118.0000002017-07-210.9900001160.525944
5238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatown40.71344-73.99037Entire home/apt150.01160.0000002019-06-091.3300004188.000000
5295Beautiful 1br on Upper West Side7702LenaManhattanUpper West Side40.80316-73.96545Entire home/apt135.0553.0000002019-06-220.43000016.000000
5441Central Manhattan/near Broadway7989KateManhattanHell's Kitchen40.76076-73.98867Private room85.02188.0000002019-06-231.500000139.000000
namehost_idhost_nameneighborhood_groupneighborhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
id
36482809Stunning Bedroom NYC! Walking to Central Park!!131529729KendallManhattanEast Harlem40.79633-73.93605Private room75.0231.7284782019-07-081.3732512353.0
36483010Comfy 1 Bedroom in Midtown East274311461ScottManhattanMidtown40.75561-73.96723Entire home/apt200.0631.7284782019-07-081.3732511176.0
36483152Garden Jewel Apartment in Williamsburg New York208514239MelkiBrooklynWilliamsburg40.71232-73.94220Entire home/apt170.0131.7284782019-07-081.3732513365.0
36484087Spacious Room w/ Private Rooftop, Central location274321313KatManhattanHell's Kitchen40.76392-73.99183Private room125.0431.7284782019-07-081.373251131.0
36484363QUIT PRIVATE HOUSE107716952MichaelQueensJamaica40.69137-73.80844Private room65.0131.7284782019-07-081.3732512163.0
36484665Charming one bedroom - newly renovated rowhouse8232441SabrinaBrooklynBedford-Stuyvesant40.67853-73.94995Private room70.0231.7284782019-07-081.37325129.0
36485057Affordable room in Bushwick/East Williamsburg6570630MarisolBrooklynBushwick40.70184-73.93317Private room40.0431.7284782019-07-081.373251236.0
36485431Sunny Studio at Historical Neighborhood23492952Ilgar & AyselManhattanHarlem40.81475-73.94867Entire home/apt115.01031.7284782019-07-081.373251127.0
3648560943rd St. Time Square-cozy single bed30985759TazManhattanHell's Kitchen40.75751-73.99112Shared room55.0131.7284782019-07-081.37325162.0
36487245Trendy duplex in the very heart of Hell's Kitchen68119814ChristopheManhattanHell's Kitchen40.76404-73.98933Private room90.0731.7284782019-07-081.373251123.0